{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T22:00:01Z","timestamp":1763762401219,"version":"3.45.0"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"11","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,11,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Non-small-cell lung cancer (NSCLC) lesions appearing solid on imaging are highly likely to be malignant. However, \u201csolid\u201d has a subjective definition. This study aimed to determine whether artificial intelligence (AI)-based imaging analysis can offer a more objective and clinically meaningful definition of solid tumours, specifically in regards to lymph node metastasis and prognosis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study included 216 patients with cN0 lung adenocarcinoma (pathological invasive diameter \u2264 30 mm) who underwent lobectomy with lymph node dissection. AI software was used to calculate the consolidation-to-tumour diameter (cD\/tD) and volume (cV\/tV) ratios, which were then compared with radiologist-defined cD\/tD. To determine the optimal cutoff values, correlations between the pathological invasive diameter\/tumour diameter ratio (PathoiD\/tD) and lymph node metastasis were evaluated. Subsequently, these values were applied to a subset of tumours measuring \u226420 mm (n\u2009=\u2009117) to determine their potential use in candidate selection for limited resection.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A cV\/tV cutoff value of \u22650.72 accurately predicted lymph node metastasis and improved concordance between AI and radiologist solid tumour assessment while maintaining a similar PathoiD\/tD to the conventional (cD\/tD\u2009=\u20091.0) threshold. Among tumours measuring \u226420 mm in the low solid-component group (cV\/tV &amp;lt;0.72), lymph node metastases were not observed; the 5-year recurrence-free survival rate was 100%.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>An AI-based volumetric analysis using a cV\/tV threshold of \u22650.72 showed potential for predicting lymph node metastasis in this single-centre retrospective study of lung adenocarcinoma \u226430 mm. External validation in diverse, multicentre cohorts is essential before clinical implementation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/ejcts\/ezaf385","type":"journal-article","created":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T12:34:45Z","timestamp":1762778085000},"source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence-Advanced Imaging for Solid-Type Lung Adenocarcinoma: Towards Greater Clinical Relevance"],"prefix":"10.1093","volume":"67","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1186-1693","authenticated-orcid":false,"given":"Tomoki","family":"Nishida","sequence":"first","affiliation":[{"name":"Department of Surgery, Teikyo University School of Medicine , Tokyo, 173-8606,","place":["Japan"]},{"name":"Department of General Thoracic Surgery, Shonan Kamakura General Hospital , Kamakura, 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